@kdnuggets: “Building a #DataScience Portfolio: Machine Learning Project Part 1 @dataquestio”
Dataquest’s founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
Building a Data Science Portfolio: Machine Learning Project Part 1

Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image.

We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.

The entire framework, including the global and local priors as well as the colorization model, is trained in an end-to-end fashion.

The output of our model is the chrominance of the image which is fused with the luminance to form the output image.

Our architecture can process images of any resolution, unlike most existing approaches based on CNN. We leverage an existing large-scale scene classification database to train our model, exploiting the class labels of the dataset to more efficiently and discriminatively learn the global priors.

@MikeTamir: “End-to-end #MachineLearning: Automatic Image Colorization with Classification #DataScience”
We present a novel technique to automatically colorize
grayscale images that combines both global priors and local
image features. Based on Convolutional Neural Networks, our
deep network features a fusion layer that allows us to
elegantly merge local information dependent on small image
patches with global priors computed using the entire image. The
entire framework, including the global and local priors as well
as the colorization model, is trained in an end-to-end fashion.
Furthermore, our architecture can process images of any
resolution, unlike most existing approaches based on CNN. We
leverage an existing large-scale scene classification database
to train our model, exploiting the class labels of the dataset
to more efficiently and discriminatively learn the global
priors. We validate our approach with a user study and compare
against the state of the art, where we show significant
improvements. Furthermore, we demonstrate our method
extensively on many different types of images, including
black-and-white photography from over a hundred years ago, and
show realistic colorizations.
Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification